Global and Local Residual Learning: Global residual learning is a skip-connection from input to the final reconstruction layer, which helps improve the transmission of information from input to output and reduce the loss of information to a certain extent. However, as the network becomes deeper, a significant amount of image details are inevitably lost after going through so many layers. Therefore, the local residual learning is proposed, which is performed in every few stacked layers instead of from input to output. In this approach, a multi-path mode is formed and rich image details are carried and also helps gradient flow. Furthermore, many new feature extraction modules have introduced the local residual learning to reinforce strong learning capabilities [1]}, [2]}. Of course, combining local residual learning and global residual learning is also highly popular now [3]}, [4]}, [2]}.
